Cluster Evaluation of Density Based Subspace Clustering

Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this app...

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Main Authors: Sembiring, Rahmat Widia, Jasni, Mohamad Zain
Format: Article
Language:English
English
Published: 2010
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
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author Sembiring, Rahmat Widia
Jasni, Mohamad Zain
author_facet Sembiring, Rahmat Widia
Jasni, Mohamad Zain
author_sort Sembiring, Rahmat Widia
collection UMP
description Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer.
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spelling UMPir12012018-05-22T02:32:02Z http://umpir.ump.edu.my/id/eprint/1201/ Cluster Evaluation of Density Based Subspace Clustering Sembiring, Rahmat Widia Jasni, Mohamad Zain QA75 Electronic computers. Computer science Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer. 2010 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering other en http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering Sembiring, Rahmat Widia and Jasni, Mohamad Zain (2010) Cluster Evaluation of Density Based Subspace Clustering. Journal of Computing, Volume 2, (Issue 11, November 2010). pp. 14-19. ISSN 2151-9617. (Published) http://umpir.ump.edu.my/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
spellingShingle QA75 Electronic computers. Computer science
Sembiring, Rahmat Widia
Jasni, Mohamad Zain
Cluster Evaluation of Density Based Subspace Clustering
title Cluster Evaluation of Density Based Subspace Clustering
title_full Cluster Evaluation of Density Based Subspace Clustering
title_fullStr Cluster Evaluation of Density Based Subspace Clustering
title_full_unstemmed Cluster Evaluation of Density Based Subspace Clustering
title_short Cluster Evaluation of Density Based Subspace Clustering
title_sort cluster evaluation of density based subspace clustering
topic QA75 Electronic computers. Computer science
url http://umpir.ump.edu.my/id/eprint/1201/1/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
http://umpir.ump.edu.my/id/eprint/1201/2/Cluster-Evaluation-of-Density-Based-Subspace-Clustering
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